End to end vision transformer architecture for brain stroke assessment based on multi-slice classification and localization using computed tomography

Ayoub, Muhammad; Liao, Zhifang; Hussain, Shabir; Li, Lifeng; Zhang, Chris W.J.; Wong, Kelvin K.L.

doi:10.1016/j.compmedimag.2023.102294

Cited by 6 publications

(1 citation statement)

References 18 publications

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“…Stroke stands among the second leading causes of death worldwide [1]- [3], affecting over 100 million people annually [4]. It ranks as the third major cause of premature death, resulting in approximately 6.2 million deaths each year [5]. This alarming trend extends to the African continent, including the North African region, where stroke poses a significant health burden.…”

Section: Introductionmentioning

confidence: 99%

Ischemic Stroke Classification Using VGG-16 Convolutional Neural Networks: A Study on Moroccan MRI Scans

Abbaoui,

Retal,

Ziti

et al. 2024

Int. J. Onl. Eng.

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This study presents a comprehensive exploration of deep learning models for precise brain ischemic stroke classification using medical data from Morocco. Following the OSEMN approach, our methodology leverages transfer learning with the VGG-16 architecture and employs data augmentation techniques to enhance model performance. Our developed model achieved a remarkable validation accuracy of 90%, surpassing alternative state-of-theart models (ResNet50: 87.0%, InceptionV3: 82.0%, VGG-19: 81.0%). Notably, all models were rigorously evaluated on the same meticulously curated dataset, ensuring fair and consistent comparisons. The investigation underscores VGG-16’s superior performance in distinguishing stroke cases, highlighting its potential as a robust tool for accurate diagnosis. Comparative analyses among popular deep learning architectures not only demonstrate our model’s efficacy but also emphasize the importance of selecting the right architecture for medical image classification tasks. These findings contribute to the growing evidence supporting advanced deep learning techniques in medical imaging. Achieving a validation accuracy of 90%, our model holds significant promise for real-world healthcare applications, showcasing the critical role of cutting-edge technologies in advancing diagnostic accuracy and the transformative potential of deep learning in the medical field.

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